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Open AccessArticle

ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information

1
College of Computer and Information Science, Southwest University, Chongqing 400715, China
2
College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(8), 2267; https://doi.org/10.3390/ijms19082267
Received: 28 June 2018 / Revised: 23 July 2018 / Accepted: 30 July 2018 / Published: 2 August 2018
(This article belongs to the Section Biochemistry)
Identifying single nucleotide polymorphism (SNP) interactions is considered as a popular and crucial way for explaining the missing heritability of complex diseases in genome-wide association studies (GWAS). Many approaches have been proposed to detect SNP interactions. However, existing approaches generally suffer from the high computational complexity resulting from the explosion of candidate high-order interactions. In this paper, we propose a two-stage approach (called ClusterMI) to detect high-order genome-wide SNP interactions based on significant pairwise SNP combinations. In the screening stage, to alleviate the huge computational burden, ClusterMI firstly applies a clustering algorithm combined with mutual information to divide SNPs into different clusters. Then, ClusterMI utilizes conditional mutual information to screen significant pairwise SNP combinations in each cluster. In this way, there is a higher probability of identifying significant two-locus combinations in each group, and the computational load for the follow-up search can be greatly reduced. In the search stage, two different search strategies (exhaustive search and improved ant colony optimization search) are provided to detect high-order SNP interactions based on the cardinality of significant two-locus combinations. Extensive simulation experiments show that ClusterMI has better performance than other related and competitive approaches. Experiments on two real case-control datasets from Wellcome Trust Case Control Consortium (WTCCC) also demonstrate that ClusterMI is more capable of identifying high-order SNP interactions from genome-wide data. View Full-Text
Keywords: genome-wide association studies; high-order SNP interactions; clustering; mutual information; improved ant colony optimization genome-wide association studies; high-order SNP interactions; clustering; mutual information; improved ant colony optimization
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Cao, X.; Yu, G.; Liu, J.; Jia, L.; Wang, J. ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information. Int. J. Mol. Sci. 2018, 19, 2267.

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